today <- Sys.Date() # today's date

Mise à jour du 2021-03-08.

Légende

library("RColorBrewer")
brw <- brewer.pal(12, "Set3")
brw[2] <- brw[12] # Darker yellow
brw[10] <- brw[11] # Lighter shade
colsAge <- c("#000000FF", brw[1:10])
names(colsAge) <- c("0", "9", "19", "29", "39", "49", "59", "69", "79", "89", "90")
pchAge <- c(16, 0:9)
names(pchAge) <- names(colsAge)
cexAge <- c(1.2, rep(1, 10))
names(cexAge) <- names(colsAge)

ages <- c("tous", "0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80-89", "90+")
par(mfrow = c(1, 1))
plot(0:1, 0:1, type = "n", axes = FALSE, xlab = "", ylab = "")
legend(x = 0.5, y = 1, legend = ages, pch = pchAge, col = colsAge)

Données France

Load data

# Données France
URL <- "https://www.data.gouv.fr/fr/datasets/r/c43d7f3f-c9f5-436b-9b26-728f80e0fd52"
dataFile <- paste0("data/France_", today, ".csv") # name file with today's date
download.file(URL, dataFile) # download file from repo
dat.France <- read.csv(dataFile, sep = ";", stringsAsFactors = FALSE)


# Format date
dat.France$date1 <- as.Date(substring(dat.France$semaine, 1, 10))
dat.France$date2 <- as.Date(substring(dat.France$semaine, 12, 21))

V1

# All ages, time
par(las = 1)
plot(dat.France$date2, dat.France$Prc_susp_501Y_V1, ylim = c(0, 100), 
     col = colsAge[as.character(dat.France$cl_age90)], 
     pch = pchAge[as.character(dat.France$cl_age90)], 
     cex = cexAge[as.character(dat.France$cl_age90)], 
     xlab = "date", ylab = "Proportion V1", axes = FALSE)
axis(1, pos = 0, at = as.Date(unique(dat.France$date2)), labels = format(unique(dat.France$date2), format = "%b %d"))
axis(2)

V2/V3

# All ages, time
par(las = 1)
plot(dat.France$date2, dat.France$Prc_susp_501Y_V2_3, ylim = c(0, 100), 
     col = colsAge[as.character(dat.France$cl_age90)], 
     pch = pchAge[as.character(dat.France$cl_age90)], 
     cex = cexAge[as.character(dat.France$cl_age90)], 
     xlab = "date", ylab = "Proportion V2/V3", axes = FALSE)
axis(1, pos = 0, at = as.Date(unique(dat.France$date2)), labels = format(unique(dat.France$date2), format = "%b %d"))
axis(2)

Données régions

URL <- "https://www.data.gouv.fr/fr/datasets/r/73e8851a-d851-43f8-89e4-6178b35b7127"
dataFile <- paste0("data/Regions_", today, ".csv") # name file with today's date
download.file(URL, dataFile) # download file from repo
dat.regions <- read.csv(dataFile, sep = ";", stringsAsFactors = FALSE)

# Format date
dat.regions$date1 <- as.Date(substring(dat.regions$semaine, 1, 10))
dat.regions$date2 <- as.Date(substring(dat.regions$semaine, 12, 21))
# Codes regions
URL <- "https://www.data.gouv.fr/en/datasets/r/34fc7b52-ef11-4ab0-bc16-e1aae5c942e7"
dataFile <- "data/coderegions.csv"
download.file(URL, dataFile)
codesRegions <- read.csv(dataFile, sep = ",", stringsAsFactors = FALSE)

# Turn into dictionary
regs <- codesRegions$nom_region
names(regs) <- as.character(codesRegions$code_region)
# Add region name
dat.regions$reg_name <- regs[as.character(dat.regions$reg)]
# dat.regions[floor(runif(10)*1000), c("reg", "reg_name")] # check a few names

# What are the other regions??
# aggregate(dat.regions$reg, by = list(dat.regions$reg), FUN = length)

V1

tmp <- unique(dat.regions$reg) # Region codes
tmp <- tmp[tmp>10 & tmp <= 93] # Choose only metropolitan regions
par(mfrow = c(4, 3))
for(region in tmp){
  subdat <- dat.regions[dat.regions$reg == region, ]
  plot(subdat$date2, subdat$Prc_susp_501Y_V1, ylim = c(0, 100), main = regs[as.character(region)], col = colsAge[as.character(subdat$cl_age90)], pch = pchAge[as.character(subdat$cl_age90)], 
       xlab = "date", ylab = "Proportion V1"
       )
}

V2

par(mfrow = c(4, 3))
for(region in tmp){
  subdat <- dat.regions[dat.regions$reg == region, ]
  plot(subdat$date2, subdat$Prc_susp_501Y_V2_3, ylim = c(0, 100), main = regs[as.character(region)], col = colsAge[as.character(subdat$cl_age90)], pch = pchAge[as.character(subdat$cl_age90)], 
       xlab = "date", ylab = "Proportion V2/V3"
       )
}

#

Départements

URL <- "https://www.data.gouv.fr/fr/datasets/r/16f4fd03-797f-4616-bca9-78ff212d06e8"
dataFile <- paste0("data/Dep_", today, ".csv") # name file with today's date
download.file(URL, dataFile) # download file from repo
dat.deps <- read.csv(dataFile, sep = ";", stringsAsFactors = FALSE)

# Format date
dat.deps$date1 <- as.Date(substring(dat.deps$semaine, 1, 10))
dat.deps$date2 <- as.Date(substring(dat.deps$semaine, 12, 21))
# Add name
deps <- read.csv("data/departement2020.csv", stringsAsFactors = FALSE)
# Turn into dictionnary
dps <- deps$libelle
names(dps) <- as.character(deps$dep)
dat.deps$departement <- dps[as.character(dat.deps$dep)]

V1

par(mfrow = c(35, 3))
for(idep in sort(unique(dat.deps$dep))){
  tmp <- dat.deps[dat.deps$dep == idep, ]
  plot(tmp$date2, tmp$Prc_susp_501Y_V1, ylim = c(0, 100), col = colsAge[as.character(tmp$cl_age90)], pch = pchAge[as.character(tmp$cl_age90)], 
       xlab = "date", ylab = "Proportion V1", 
       main = unique(tmp$departement))
  
#  legend(x = min(dat.deps$date2), y = 100, legend = ages, pch = pchAge, col = colsAge)
}

V2/V3

par(mfrow = c(35, 3))
for(idep in sort(unique(dat.deps$dep))){
  tmp <- dat.deps[dat.deps$dep == idep, ]
  plot(tmp$date2, tmp$Prc_susp_501Y_V2_3, ylim = c(0, 100), col = colsAge[as.character(tmp$cl_age90)], pch = pchAge[as.character(tmp$cl_age90)], 
       xlab = "date", ylab = "Proportion V2/V3", 
       main = unique(tmp$departement))
  
#  legend(x = min(dat.deps$date2), y = 100, legend = ages, pch = pchAge, col = colsAge)
}